diff_diff.WildBootstrapResults#

class diff_diff.WildBootstrapResults[source]#

Bases: object

Results from wild cluster bootstrap inference.

se#

Analytical cluster-robust (CR1) standard error of the coefficient. The wild bootstrap studentizes the test with this SE; it is not a rescaled bootstrap dispersion.

Type:

float

p_value#

Wild cluster bootstrap p-value (two-tailed or equal-tailed).

Type:

float

t_stat_original#

Studentized statistic of the original estimate, (coef - null) / se.

Type:

float

ci_lower#

Lower bound of the confidence interval (by test inversion).

Type:

float

ci_upper#

Upper bound of the confidence interval (by test inversion).

Type:

float

n_clusters#

Number of clusters in the data.

Type:

int

n_bootstrap#

Number of bootstrap replications.

Type:

int

weight_type#

Type of bootstrap weights used (“rademacher”, “webb”, or “mammen”).

Type:

str

alpha#

Significance level used for confidence interval.

Type:

float

p_val_type#

Test shape used (“two-tailed” or “equal-tailed”).

Type:

str

bootstrap_distribution#

Bootstrap distribution of the studentized statistic t* evaluated at the null (if requested).

Type:

np.ndarray, optional

References

Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-Based Improvements for Inference with Clustered Errors. The Review of Economics and Statistics, 90(3), 414-427.

Methods

__init__(se, p_value, t_stat_original, ...)

print_summary()

Print formatted summary to stdout.

summary()

Generate formatted summary of bootstrap results.

Attributes

alpha

bootstrap_distribution

p_val_type

se

p_value

t_stat_original

ci_lower

ci_upper

n_clusters

n_bootstrap

weight_type

__init__(se, p_value, t_stat_original, ci_lower, ci_upper, n_clusters, n_bootstrap, weight_type, alpha=0.05, p_val_type='two-tailed', bootstrap_distribution=None)#
Parameters:
Return type:

None

classmethod __new__(*args, **kwargs)#